Bibi Haim, Nutman Amir, Shoseyov David, Shalom Mendel, Peled Ronit, Kivity Shmuel, Nutman Jacob
Barzilai Medical Center, Ashkelon, Israel.
Chest. 2002 Nov;122(5):1627-32. doi: 10.1378/chest.122.5.1627.
Accurate prediction of the effect of atmospheric changes, including pollutants, on emergency department (ED) visits for respiratory symptoms would be useful, but has proven difficult. The main difficulty is the limitation of the classical linear models and logistic regression with multiple variables to handle the multifactorial effect.
To predict ED visits, we have created a computer-based model called an artificial neural network (ANN) using a back-propagation training algorithm and genetic algorithm optimization. This ANN was fed meteorologic and air pollution input variables and trained to predict the number of patients admitted to the ED with respiratory symptoms of asthma, COPD, and acute and chronic bronchitis on the corresponding day. One thousand twenty data sets were extracted from an ED admittance database at the Barzilai Medical Center (Ashkelon, Israel), and randomized to a network training set (n = 816) and a test set (n = 204).
The neural network performed best when the predictor variables used were temperature, relative humidity, barometric pressure, SO(2), and oxidation products of nitric oxide, and the data presented as peak value 24 h prior to ED admission and the average during the 7 days before the ED visit. The neural network was able to predict the test set with an average error of 12%.
Based on meteorologic and pollution data, the use of an ANN can assist in the prediction of ED visits related to respiratory conditions.
准确预测包括污染物在内的大气变化对因呼吸道症状前往急诊科（ED）就诊的影响会很有帮助，但事实证明这很困难。主要困难在于经典线性模型和多变量逻辑回归在处理多因素影响方面存在局限性。
为了预测急诊科就诊情况，我们使用反向传播训练算法和遗传算法优化创建了一个名为人工神经网络（ANN）的计算机模型。该人工神经网络输入气象和空气污染变量，并经过训练以预测相应日期因哮喘、慢性阻塞性肺疾病（COPD）以及急慢性支气管炎等呼吸道症状而入住急诊科的患者数量。从以色列阿什凯隆的巴齐莱医疗中心的急诊科入院数据库中提取了1020个数据集，并随机分为网络训练集（n = 816）和测试集（n = 204）。
当使用的预测变量为温度、相对湿度、气压、二氧化硫（SO₂）以及一氧化氮的氧化产物，且数据以急诊科入院前24小时的峰值和急诊科就诊前7天的平均值呈现时，神经网络表现最佳。该神经网络能够以12%的平均误差预测测试集。
基于气象和污染数据，使用人工神经网络有助于预测与呼吸道疾病相关的急诊科就诊情况。
